Systems and Methods for Providing Professional Treatment Guidance for Diabetes Patients

ABSTRACT

Systems and methods are provided for providing diabetes patient treatment guidance for a patient in which a biochemical data set is obtained. The biochemical data set comprises test results from a single blood draw of the patient including at least three measurements selected from the set: a high-sensitivity c-reactive protein test, an adiponectin level test, an intact proinsulin level test, an insulin level test, a C-peptide test, a HbA1c test, and an eGFR level test. A demographic data set for the patient is also obtained that comprises the patient&#39;s gender and diabetes stage. The biochemical data set and demographic data set is run against one or more rules to determine a first patient therapy pattern. Then, a report is prepared based on an identity of the first therapy patient pattern. The report sets priorities among intervention classes for the patient based on the identity of the first patient pattern.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/179,429, filed on Nov. 2, 2018, entitled “Systems and Methods forProviding Professional Treatment Guidance for Diabetes Patients,” whichclaims priority to U.S. Provisional Patent Application No. 62/580,889,filed on Nov. 2, 2017, entitled “Systems and Methods for ProvidingProfessional Treatment Guidance for Diabetes Patients,” each of which ishereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods forproviding Type 2 diabetes patient treatment guidance to professionals.

BACKGROUND

Type 2 diabetes mellitus is characterized by progressive disruption ofnormal physiologic insulin secretion and/or a significant cellularresistance to the action of insulin. The relative contribution of eachof these factors in the resulting elevation of blood sugar can beimportant in selecting the proper diabetes therapy.

In healthy individuals, basal insulin secretion by pancreatic (3-cellsoccurs continuously to maintain steady glucose levels for extendedperiods between meals. Also in healthy individuals, there is prandialsecretion in which insulin is rapidly released in an initial first-phasespike in response to a meal, followed by prolonged insulin secretionthat returns to basal levels after 2-3 hours.

Insulin is a hormone that binds to insulin receptors to lower bloodglucose by facilitating cellular uptake of glucose, amino acids, andfatty acids into skeletal muscle and fat and by inhibiting the output ofglucose from the liver. In normal healthy individuals, physiologic basaland prandial insulin secretions maintain euglycemia, which affectsfasting plasma glucose and postprandial plasma glucose concentrations.Basal and prandial insulin secretion is impaired in Type 2 diabetes andearly post-meal response is absent. To address these adverse events,subjects with Type 2 diabetes are provided with insulin medicamenttreatment regimens. The goal of these insulin medicament treatmentregimens is to maintain a desired fasting blood glucose target levelthat will minimize estimated risk of hypo- and hyper-glycaemia.

There are a number of drugs available to treat diabetes in addition toinsulin medicament. Which drugs are prescribed to a given subjectdepends on the stage of therapy of the subject, the subject's overallhealth, side effect risks, mode of administration, cost and formularyavailability, as well as a number of physiological pathwayconsiderations, typically assessed through measurements of one or moreblood markers. Treatment regimens for each patient are ideallycustomized for the patient dependent upon these factors. However,conventional methods for devising a treatment regimen and sortingthrough the vast array of possible therapies that can be prescribed to aparticular patient in need of treatment are presently unsatisfactory. Ina typical situation, a doctor advising a particular patient would ordertwo or three different tests to ascertain the condition of a diabetespatient. The metabolic pathophysiology is typically measured only inpart, or much more often completely neglected. For instance, the doctormay order an A1C test and a renal function test and decide, based onthis, to institute therapy or make a change in the therapeutic regimen.If the case were somewhat unusual, the physician may further orderadditional tests (e.g., Adiponectin, C-peptide, hsCRP, etc.). Thus, inthe most extended cases, the physician may then have four or five piecesof information, often taken at different time points. (e.g., five testresults). While in some situations, the physician will eventually orderone or more other tests, the drawback is that the physician does notorder these tests all at once and the test results are not integratedinto any sort of overall view of the patient condition that can then berelated to a drug treatment plan. Thus, one drawback with conventionaltreatment of the diabetic condition in subjects is the acquisition ofthe relevant information regarding the condition of the subject in apiecemeal, ad hoc, basis that does not lend itself to determining theoverall picture of the subject's condition. Another drawback withconventional treatment of the diabetic condition is the limiteddatabase/knowledge on drug classes, limited availability of experts andlimited capability to quickly handle the complex nature of anti-diabeticdrug data. In multi-drug therapy, the number of choices for a clinicianto handle at the drug class level is over 200 alternate regimens, andmost classes have multiple suppliers, with some differences between thesuppliers.

Furthermore, conventional treatment of the diabetic condition is limitedto improving a handful of particular patient conditions, such asimproving a precise A1C level (e.g., 6.8% improved to 5.4%). Theseparticular patient conditions are one dimensional, in that eachcondition describes an exact condition of the patient. If more than onepatient condition is to be improved, determining how to treat theseconditions individually and in combination becomes rapidly more complex.Accordingly, there exists a need to tailor treatment guidance plans forone or more multi-dimensional conditions, such as general insulinresistance, general beta-cell stress, general cardiovascular risk, etc.,in order to determine a general trend of each multi-dimensionalcondition instead of a specific trend for multiple individualconditions.

Given the above background, what is needed in the art are systems andmethods for assessing the condition of a diabetic subject and based onthis assessment, offering a suitable personalized treatment plan for thesubject.

SUMMARY

The present disclosure addresses the need in the art for systems andmethods for assessing the condition of a diabetic subject and offering asuitable treatment plan for the subject based on this assessment. Toaccomplish this, some embodiments of the present disclosure make aninformed selection (e.g., from among over 23,000 differentclassification patterns). Each classification pattern represents adifferent permutation of the possible results of the classificationvariables that are considered by the systems and methods of the presentdisclosure. A database of these permutations lists the permutationsindividually, and each such respective permutation is associated with aunique report specific to the respective permutation.

In accordance with the present disclosure, a report is generated for ahealth professional when a data set is loaded by a partner laboratoryinto the web portal for the report database. The data is analyzed tocreate a patient profile, which consists of the pattern of the results.Each pattern has multiple dimensions (e.g., insulin resistance,cardiovascular inflammation, renal condition, etc.) and multipleclassifications of such dimensions (e.g., severe, moderately impaired,etc.). The profile, through physician judgement derived decision rules,is linked to a database of information relevant to clinical practice onthe major anti-diabetic drug classes. The linkage results in specificcontent assigned to the report, including an ordering of preferred drugclasses, drawn from a content database. This content is divided intomany different content blocks, which are used to populate the content ofa complete report using complex decision trees, maps, and subsets of thecontent database. Some of these content blocks map from some set ofresults to content, or from a set of results to a classification ofcondition, which is then mapped to content. For example, some of thevariables are used to create insulin resistance categories (severe,significant, etc.), which in turn are used to construct a pattern ofother factors, encompassing the major aspects of patient condition,which is mapped to specific choices both of drug class order in the drugrecommendations sections, and the physician course of action section.Thus there are four steps in this case from a test result of an analyteto specific content in the report (test result to aspect of patientcondition to overall patient clinical status to suggested drug order).Other content blocks are static, or drawn from a much more limited setof possibilities. Some map from a result or group of results to content,while others, such as references, are tied to specific wording.

All of these steps and routes to populate content are programmed inaccordance with the disclosed systems and methods. In this way all ofthe content blocks of the report are populated with specific content assoon as a set of results is received. The disclosed systems and methodsthen generate a report (e.g. in PDF format) that is, in someembodiments, sent electronically to the requesting laboratory. In someembodiments, the database is completely tracked for all changes, andeditable by the administrators so that it can be kept up to date.

In the systems and methods in accordance with one aspect of the presentdisclosure, a patient profile is obtained for a subject in need oftreatment or treatment modification. In some embodiments, the patientprofile comprises a panel of seven tests that are run from a singleblood drawn from the patient. In some embodiments, the resultingbiochemistry profile is supplemented by two demographic variables (i)gender and (ii) stage of disease or drug therapy. In some embodiments,partial profiles are not permitted, the data set must be submittedcomplete (including the seven tests and the two demographic variables),or it is rejected. In some embodiments, each algorithm used in thesystems and methods of the present disclosure is adjusted for theindividual laboratory cut-offs (binning criteria) that are derived fromthe assay methods and population adjustments particular to eachlaboratory's service population. In some embodiments, the seven teststhat are run from a single blood draw are high-sensitivity c-reactiveprotein, adiponectin, intact proinsulin, insulin, C-peptide, HbA1c, andthe eGFR. In some embodiments, the tests include one or more additionalanalytes that define a metabolic condition of a patient, such as a brainanalyte, a gut analyte, a beta cell analyte, a liver analyte, a kidneyanalyte, and/or a cardiovascular analyte.

Another aspect of the present disclosure provides a method for ahealthcare profession with a patient treatment plan. The method includesobtaining a biochemical data set, which includes a plurality of testresults from a single blood draw of the patient, and a demographic dataset for the patient. The plurality of test results of the biochemicaldata set includes at least three measurements from the group consistingof a high-sensitivity c-reactive protein test, an adiponectin leveltest, a proinsulin level test, an insulin level test, a C-peptide test,a HbA1c test, and an eGFR level test. The demographic data set includesan indication of a gender of the patient and an enumerated indication ofa stage of disease or therapy of the patient. All or a portion of thebiochemical data set and the demographic data set are run against asubset of decision rules in a plurality of decision rules. In accordancewith a determination that one or more firing conditions of a respectivedecision rule in the subset of decision rules is fired, a correspondingpatient pattern in a plurality of patient patterns is determined as afirst patient pattern. A report is prepared based on an identity of thefirst patient pattern. The report provides a prioritization ofintervention class in a priority ordering of intervention classes basedon the identity of the first patient pattern.

In some embodiments, the plurality of test results include three, four,five, six, or seven measurements from the group consisting of ahigh-sensitivity c-reactive protein test, an adiponectin level test, aproinsulin level test, an insulin level test, a C-peptide test, an HbA1ctest, and an eGFR test.

In some embodiments, the plurality of test results consists ofmeasurements from a high-sensitivity c-reactive protein test, anadiponectin level test, a proinsulin level test, an insulin level test,a C-peptide test, an HbA1c test, and an eGFR test.

In some embodiments, the plurality of test results consists ofmeasurements of eight, nine, ten, eleven, twelve, thirteen, fourteen, orfifteen measurements from a high-sensitivity c-reactive protein test, anadiponectin level test, a proinsulin level test, an insulin level test,a C-peptide test, an HbA1c test, an eGFR test, and one or more analytesthat define a dimension of a patient metabolic condition including abrain analyte, a gut analyte, a beta cell analyte, a liver analyte, akidney analyte, and a cardiovascular analyte.

In some embodiments, the enumerated indication of a patient's stage ofdisease or therapy is one of (i) diagnosed as pre-diabetes, (ii)diagnosed with diabetes but not taking a drug (iii) diagnosed withdiabetes and taking a first line diabetes drug (iv) diagnosed withdiabetes and prescribed multiple diabetes drugs without insulin and (v)diagnosed with diabetes and prescribed multiple diabetes drugs withinsulin.

In some embodiments, the prioritization of intervention class includes aprioritization of one or more drug classes including a metformin class,a sodium-glucose cotransporter-2 inhibitor class, a glucagon-likepeptide-1 receptor agonists class, a dipeptidyl peptidase-4 inhibitorclass, an insulin class, a thiazolidinedione class, a glinides class,and a sulfonylureas class.

In some embodiments, the prioritization of intervention class includes aprioritization of one or more drugs in a respective drug class.

In some embodiments, one or more decision rules in the plurality ofdecision rules is determined according to an analysis of the biochemicaldata set and the demographic data of each patient across a cohortpopulation of patients conducted by a plurality of expert physicians.

In some embodiments, one or more decision rules in the plurality ofdecision rules is determined according to an analysis of at least a peerreviewed reference pertaining to a drug class for treatment of diabetes.

In some embodiments, the prioritization of intervention class includes aprioritization of one or more drug classes, exercise, and diet.

In some embodiments, the report provides a magnitude of anticipatedefficacy of the first patient pattern with respect to one or morepatient metabolic conditions identified by the biochemical data set.

In some embodiments, the report comprises a plurality of sections. Eachsection in the plurality of sections is classified as a static sectionthat includes predetermined information, a dynamic section that includespredetermined information as determined by one or more decision rules inthe plurality of decision rules, or a reference section that includesinformation provided from one or more databases that is accessible tothe computer.

In some embodiments, each firing condition in the one or more firingconditions of a respective decision rule in the plurality of decisionrules includes one or more conditions selected from the group consistingof a diabetes stage of the patient, a number of medications currentlybeing taken by the patient, a dosage of a medication currently beingtaken by the patient a type of medications currently being taken by thepatient, a type of medications previously taken by the patient, and apatient metabolic condition.

In some embodiments, the patient metabolic condition is classified on anon-dimensional scale.

Yet another aspect of the present disclosure provides a method forproviding a healthcare professional with a metabolic assessment of apatient. In some embodiments the patient has not been diagnosed withdiabetes. In this aspect of the present disclosure, at a computercomprising one or more processors and a memory, a biochemical data setis obtained. The biochemical data set comprises a plurality of testresults from a single blood draw of the patient. The plurality of testresults comprises at least three measurements from the group consistingof a high-sensitivity c-reactive protein test, an adiponectin leveltest, a proinsulin level test, an insulin level test, a C-peptide test,a HbA1c test, and an eGFR level test. A demographic data set for thepatient is obtained. The demographic data set comprises an indication ofa gender of the patient. The biochemical data set and demographic dataset is used to map the patient onto a first patient pattern in aplurality of patient patterns. A report is then prepared based on anidentity of the first patient pattern. The report provides a metabolicstatus of the patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system topology for providing diabetespatient treatment guidance that includes a diabetes patient careguidance device 250 for providing the treatment guidance, a datacollection apparatus 200 for collecting patient biochemical data anddemographic data, one or more sensors 102 for measuring the patientbiochemical data, and one or more apparatus 104 for obtaining thedemographic data, where the above-identified components areinterconnected, optionally through a communications network, inaccordance with an embodiment of the present disclosure.

FIG. 2A illustrates an apparatus for providing diabetes patienttreatment guidance for a subject in accordance with an embodiment of thepresent disclosure.

FIG. 2B illustrates a decision rule database for providing diabetespatient treatment guidance for a patient in accordance with anotherembodiment of the present disclosure.

FIG. 3 illustrates a communication scheme between various databases andmodules of the diabetes patient care guidance device 250, in accordancewith an embodiment of the present disclosure.

FIG. 4 provides a flow chart of processes and features of an apparatusfor providing diabetes patient treatment guidance for a patient, whereoptional elements of the flow chart are indicated by dashed boxes, inaccordance with various embodiments of the present disclosure.

FIG. 5A illustrates an example integrated system of connectedmeasurement devices, demographic information intake devices, memory anda processor for providing diabetes patient treatment guidance for apatient, in accordance with an embodiment of the present disclosure.

FIGS. 5B, 5C, and 5D illustrate a biochemical test result, in accordancewith an embodiment of the present disclosure.

FIG. 6 illustrates the effect that different forms of anti-diabeticagent interventions respectively have on (i) fasting glucose levels,(ii) oral glucose tolerance test results, and (iii) HbA1c test resultsfrom a pre-administration baseline, in which, in conjunction with FIG.7, size of arrow shows magnitude of effect: large, intermediate, orsmall, and shading of arrow or circle shows the type of effect: solidfill being beneficial, no fill being adverse, and hashed being neutral,in accordance with an embodiment of the present disclosure.

FIG. 7 illustrates the affect that different forms of anti-diabeticagent interventions respectively have on (i) proinsulin levels, (ii)C-peptide levels, (iii) insulin levels, (iv) adiponectin levels, andhigh-sensitivity CRP levels, in which size of arrow shows magnitude ofeffect: large, intermediate, or small, and shading of arrow or circleshows the type of effect: solid fill being beneficial, no fill beingadverse, and hashed being neutral, in accordance with an embodiment ofthe present disclosure.

FIG. 8 illustrate examples of decision rules that map patient patternsof insulin resistance, β-cell stress level, and cardiovascularinflammation to proposed drug class orders (M=metformin, S=SGLT-2inhibitors, G=GLP-1 receptor agonists, D=DPP-4 inhibitors, I=insulin,T=thiazolidinediones, and F=Sulfonylureas, and the position of thislatter class also is used for Glinides) in which profile combinations ofdimensions that define types of profiles map to the proposed drug classorders of drug classes associated with each profile, in accordance withan example embodiment of the present disclosure.

FIG. 9 illustrates a reports database that provides template reports asa collective function of (i) therapy/disease state, (ii) gender, (iii)hs-CRP levels, (iv) adiponectin levels, (v) insulin levels, (vi)C-peptide levels, (vii) HbA1c levels, and (viii) eGFR levels inaccordance with an embodiment of the present disclosure.

FIGS. 10A, 10B, 10C, 10D, 10E, 10F, 10G, 10H, 10I, and 10J collectivelyillustrate an example report that provides diabetes patient treatmentguidance for a male patient that has diabetes and that is presentlytaking multiple drugs with insulin, in accordance with an embodiment ofthe present disclosure.

FIGS. 10K, and 10L collectively illustrate an example report thatprovides diabetes patient treatment guidance for a female patient, inaccordance with an embodiment of the present disclosure.

FIG. 11 illustrates a biomarker schema used to organize and processpatient data in order to prepare a report that provides diabetes patienttreatment guidance for a patient in accordance with an embodiment of thepresent disclosure.

FIG. 12 illustrates a report content schema used to prepare a reportthat provides diabetes patient treatment guidance for a patient inaccordance with an embodiment of the present disclosure.

FIG. 13 illustrates a report content schema, in which each patient canhave multiple panels in order to temporally track disease course, toprepare a report that provides diabetes patient treatment guidance for apatient in accordance with an embodiment of the present disclosure.

FIG. 14 illustrates a graphical representation of diabetes staging inrelation to one or more patient conditions including insulin resistance,beta-cell secretion, and active insulin, in accordance with anembodiment of the present disclosure.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

The present disclosure provides systems and methods for providingdiabetes patient treatment guidance for a healthcare provider which maybe supplemented with appropriate educational materials for the patient.FIG. 1 illustrates an example of an integrated system 502 for providingdiabetes patient treatment guidance, and FIG. 5 provides more details ofsuch a system 502. The integrated system 502 includes one or moreconnected blood test measurement devices 102, one or more demographicdata intake devices 104, memory 506, and a processor (not shown) forproviding diabetes patient treatment guidance.

With the integrated system, diabetes patient treatment guidance isprovided for a patient. For instance, a biochemical data set is obtainedfrom a single blood sample using one or more blood test measurementinput devices 102. The biochemical data set comprises test results fromat least a single blood draw of the patient including at least threemeasurements, at least four measurements, at least five measurements, atleast six measurements, or seven measurements selected from the set: ahigh-sensitivity c-reactive protein test, an Adiponectin level test, aproinsulin level test, an insulin level test, a C-peptide test, a HbA1ctest, and an eGFR level test. For example, where seven measurements arein the dataset, they include high-sensitivity c-reactive protein testresults, adiponectin level test results, proinsulin level test results,an insulin level test results, C-peptide test results, HbA1c testresults, and eGFR level test results from a single blood draw.Additional details and information related to the biochemical data setand test results will be described in more detail infra, with particularreference to at least FIGS. 5B, 5C, and 5D. A demographic data set forthe patient is also obtained using the demographic intake device 104. Inembodiments, where the goal is ranked ordered drug classes for diabeticpatient treatment, the demographic data comprises the patient's genderand diabetes stage. In embodiments where the goal is to assess themetabolic state of the patient, the demographic data comprises thepatient's gender but may not include diabetes stage. The biochemicaldata set and demographic data set is used to map the patient onto afirst patient pattern in a plurality of patient patterns. Then, a reportis prepared based on an identity of the first patient pattern. In someembodiments (e.g., where the goal is ranked ordered drug classes fordiabetic patient treatment), the report prioritizes intervention classesfor the patient based on the identity of the first patient pattern. Insome embodiments (e.g., where the goal is to assess the metabolic stateof the patient), the report provides a metabolic state of the subject.

An advantage of the present disclosure is that the report is easilytranslated into different languages.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. However, it will beapparent to one of ordinary skill in the art that the present disclosuremay be practiced without these specific details. In other instances,well-known methods, procedures, components, circuits, and networks havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first patient could be termed asecond patient, and, similarly, a second patient could be termed a firstpatient, without departing from the scope of the present disclosure. Thefirst patient and the second patient are both patients, but they are notthe same patient. Furthermore, the terms “subject,” “user,” and“patient” are used interchangeably herein.

The terms “device” and apparatus are used interchangeably herein.

The terminology used in the present disclosure is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the invention. As used in the description of the inventionand the appended claims, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will also be understood that the term “and/or”as used herein refers to and encompasses any and all possiblecombinations of one or more of the associated listed items. It will befurther understood that the terms “comprises” and/or “comprising,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if [astated condition or event] is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

A detailed description of a system 48 for providing diabetes patienttreatment guidance for a patient in accordance with the presentdisclosure is described in conjunction with FIGS. 1 through 3. As such,FIGS. 1 through 3 collectively illustrate the topology of the system inaccordance with the present disclosure. In the topology, there is adiabetes patient care device 250 for providing diabetes patienttreatment guidance for a patient (FIGS. 1, 2, and 3), a device for datacollection (“data collection device 200”), one or more blood testmeasurement devices 102 for obtaining a biochemical data set (FIGS. 1and 5), and one or more demographic data intake devices for obtainingdemographic data. Throughout the present disclosure, the data collectiondevice 200 and the diabetes patient care guidance device 250 will bereferenced as separate devices solely for purposes of clarity. That is,the disclosed functionality of the data collection device 200 and thedisclosed functionality of the diabetes patient care guidance device 250are contained in separate devices as illustrated in FIG. 1. However, itwill be appreciated that, in fact, in some embodiments, the disclosedfunctionality of the data collection device 200 and the disclosedfunctionality of the diabetes patient care guidance device 250 arecontained in a single device.

Referring to FIG. 1, the diabetes patient care guidance device 250provides diabetes patient treatment guidance for a patient. To do this,the data collection device 200, which is in electronic communicationwith patient care guidance device 250, receives blood test measurementsoriginating from one or more measurement input devices 102. In someembodiments, the blood test measurements are taken from a single blooddraw. For instance, in alternative embodiments, the data collectiondevice 200 receives blood test measurements originating from one or moremeasurement input devices 102, where the blood test measurements arefrom a plurality of blood draws taken at the same time. Further, thedata collection device receives a demographic data set for the patientfrom one or more demographic data intake devices 104. The demographicdata set includes (i) an indication of a gender of the patient and/or(ii) an indication of a diabetes stage of the patient. For instance, insome embodiments the demographic data set includes only an indication ofa gender of the patient (e.g., for determining a metabolic status of thepatient). In some embodiments, the data collection device 200 receivesthe biochemical data and demographic data directly from the blood testmeasurement devices 102 and demographic data intake devices 104. Forinstance, in some embodiments the data collection device 200 receivesthis data wirelessly through radio-frequency signals. In someembodiments such signals are in accordance with an 802.11 (WiFi),Bluetooth, or ZigBee standard. In some embodiments, the data collectiondevice 200 receives such data directly, analyzes the data, and passesthe analyzed data to the diabetes patient care guidance device 250.

In some embodiments, the data collection device 200 and/or the diabetespatient care guidance device 250 is not proximate to the patient and/ordoes not have wireless capabilities or such wireless capabilities arenot used for the purpose of acquiring the biochemical data or thedemographic data. In such embodiments, a communication network 106 maybe used to communicate the biochemical data from the one or more bloodtest measurement input devices 102 to the data collection device 200and/or the diabetes patient care device 250 and demographic data fromthe one or more data intake devices to the data collection device 200and/or the diabetes patient care guidance device 250.

Examples of networks 106 include, but are not limited to, the World WideWeb (WWW), an intranet and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN) and/or ametropolitan area network (MAN), and other devices by wirelesscommunication. In some embodiments network 106 is a body area network(BAN) or a personal area network (PAN). The wireless communicationoptionally uses any of a plurality of communications standards,protocols and technologies, including but not limited to Global Systemfor Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE),high-speed downlink packet access (HSDPA), high-speed uplink packetaccess (HSDPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-CellHSPA (DC-HSPDA), long term evolution (LTE), near field communication(NFC), wideband code division multiple access (W-CDMA), code divisionmultiple access (CDMA), time division multiple access (TDMA), Bluetooth,Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11ac, IEEE802.11ax, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice overInternet Protocol (VoIP), Wi-MAX, a protocol for e-mail (e.g., Internetmessage access protocol (IMAP) and/or post office protocol (POP)),instant messaging (e.g., extensible messaging and presence protocol(XMPP), Session Initiation Protocol for Instant Messaging and PresenceLeveraging Extensions (SIMPLE), Instant Messaging and Presence Service(IMPS)), and/or Short Message Service (SMS), or any other suitablecommunication protocol, including communication protocols not yetdeveloped as of the filing date of the present disclosure.

In some embodiments, the blood test measurement device is attached tothe patient and the data collection device 200 and/or the diabetespatient care guidance device 250 is part of the measurement device. Thatis, in some embodiments, the data collection device 200 and/or thediabetes patient care guidance device 250 and the blood test measurementdevice 102 are a single device. In some embodiments, the data collectiondevice 200 and/or the diabetes patient care guidance device 250 is partof an insulin pen or other form of intervention delivery device. Moretypically, blood test measurement devices and demographic data intakedevices 104 as well as data collection device 200 are part of a singlelaboratory and/or a single laboratory company, and the diabetes patientcare guidance device 250 is part of a consulting service that isremotely located and independent of the laboratory.

Of course, other topologies of the system 48 are possible. For instance,rather than relying on a communications network 106, the one or moreblood test measurement devices 102 and the data intake devices 104 maywirelessly transmit information directly to the data collection device200 and/or the diabetes patient care guidance device 250. Further, thedata collection device 200 and/or the diabetes patient care device 250may constitute a portable electronic device, a server computer, or infact constitute several computers that are linked together in a networkor be a virtual machine in a cloud computing context. As such, theexemplary topology shown in FIG. 1 merely serves to describe thefeatures of an embodiment of the present disclosure in a manner thatwill be readily understood to one of skill in the art.

Referring to FIG. 2, in typical embodiments, the diabetes patient careguidance device 250 comprises one or more computers. For purposes ofillustration in FIG. 2, the diabetes patient care guidance device 250 isrepresented as a single computer that includes all of the functionalityfor providing diabetes patient treatment guidance for a patient.However, the disclosure is not so limited. In some embodiments, thefunctionality for providing diabetes patient treatment guidance for apatient is spread across any number of networked computers and/orresides on each of several networked computers and/or is hosted on oneor more virtual machines at a remote location accessible across thecommunications network 106 of skill in the art will appreciate that anyof a wide array of different computer topologies are used for theapplication and all such topologies are within the scope of the presentdisclosure.

Turning to FIG. 2 and FIG. 3 with the foregoing in mind, an exemplarydiabetes patient care guidance device 250 for providing diabetes patienttreatment guidance for a patient comprises one or more processing units(CPU's) 274, a network or other communications interface 284, a memory192 (e.g., random access memory), one or more magnetic disk storageand/or persistent devices 290 optionally accessed by one or morecontrollers 288, one or more communication busses 213 forinterconnecting the aforementioned components, a user interface 278, theuser interface 278 including a display 282 and input 280 (e.g.,keyboard, keypad, touch screen), and a power supply 276 for powering theaforementioned components. In some embodiments, data in memory 192 isseamlessly shared with non-volatile memory 290 using known computingtechniques such as caching. In some embodiments, memory 192 and/ormemory 290 includes mass storage that is remotely located with respectto the central processing unit(s) 274. In other words, some data storedin memory 192 and/or memory 290 may in fact be hosted on computers thatare external to the diabetes patient care guidance device 250 but thatcan be electronically accessed by the diabetes patient care guidancedevice 250 over an Internet, intranet, or other form of network orelectronic cable (illustrated as element 106 in FIG. 2) using networkinterface 284.

In some embodiments, the memory 192 of the diabetes patient careguidance device 250 for providing diabetes patient treatment guidancefor a patient stores:

-   -   an operating system 202 that includes procedures for handling        various basic system services;    -   a diabetes patient treatment guidance module 204 that        facilitates generating a report for a respective patient, and        storing a one or more previously generated reports;    -   a patient profile database 206 for a plurality of patients, the        patient profile database comprising for each patient 207 in the        plurality of patients, one or more panel 208, each such panel        associated with a date of blood draw 210, a biochemical data set        212 based on this blood draw, and a demographic data set 238,        the biochemical data set 212 comprising hs c-reactive protein        test results 214, adiponectin level test results 216, proinsulin        level test results 218, insulin level test results 220,        C-peptide level test results 222, HbA1c level test results 224,        and/or eGFR level test results 226, and the demographic data set        228 comprising patient gender 230 and the diabetes stage of the        patient and/or stage of drug therapy 232 of the patient;    -   a decision rule database 234 that comprises a plurality of        decision rules 302, each decision rule 302 comprises a plurality        of decision rule firing conditions 304 and corresponding patient        treatment guidance 308; and    -   a drug class database 310 that comprises a listing of one or        more drug classes (e.g., metformin class, DDP-4 class, etc.).

In some embodiments, the diabetes patient treatment guidance module 204is accessible within any browser (phone, tablet, laptop/desktop). Insome embodiments, the diabetes patient treatment guidance module 204runs on native device frameworks, and is available for download onto adiabetes patient care guidance device 250 running an operating system202 such as Android or iOS. In some embodiments, the diabetes patienttreatment guidance module 204 communicates with the patient profiledatabase 206 and the decision rule database 234 in order to facilitategenerating a report for each respective patient. In some embodiments,each generated report is stored in within the diabetic patient treatmentguidance module 204 for future use. These future uses include creating ahistorical record of reports for each respective patient, as well asanalyzing past reports and current patient status to determine trends oftreatment guidance efficacy.

In some embodiments, the patient profile database 206 comprises acollection of patient profiles. Each patient profile includes aplurality of markers (e.g., analytes), that are determined from abiochemical test (e.g., test results of FIGS. 5B, 5C, and 5D) and/orprovided by the respective patient. Each marker describes a conditionand/or aspect of the patient, and may include one or more gradations ofthe respective marker. For instance, in some embodiments more than oneset of biochemical data is provided for a patient, and accordingly agradation is determined from one or more changes in the sets ofbiochemical data (e.g., a large increase in sodium level yields a largegradation). In some embodiments, each marker includes an associatednon-dimensional indicator (e.g., severe, moderately impaired, elevated,depressed, etc.). In some embodiments, each patient pattern reflects aparticular mapping of patient conditions, such that each possiblepermutation of one or more patient conditions is associated with arespective patient pattern (e.g., a low insulin level and high albuminto creatinine ratio is associated with a first patient pattern, a lowinsulin level and low albumin to creatinine ratio is associated with asecond patient pattern, a high insulin level and high albumin tocreatinine ratio is associated with a third patient pattern, etc.) Insome embodiments, the patient profile database 206 comprises a pluralityof patient patterns. In some embodiments, the plurality of patientpatterns comprises at least 10,000 patient patterns. In someembodiments, the plurality of patient patterns comprises at least 15,000patient patterns. In some embodiments, the plurality of patient patternscomprises at least 20,000 patient patterns. In some embodiments, theplurality of patient patterns comprises at least 25,000 patientpatterns.

In some embodiments, the decision rule database 234 stores one or moredecisions rules 302. Each decision rules comprises one or more decisionrule firing conditions 304. In accordance with a determination that adecision rule firing condition 304 is satisfied, the respective decisionrule 302 is fired. In some embodiments, firing of a decision rule 302up-weights an associated treatment guidance plan and/or patient pattern.Moreover, in some embodiments each decision rule is derived from anopinion of an expert physician (e.g., includes physicianrecommendations). For instance, in some embodiments a plurality ofexpert physicians determines one or more decision rules 302. In someembodiments, the one or more decision rules are determined on aconsensus basis within the plurality of expert physicians. In someembodiments, each decision rule 302 and treatment guidance 308 have aone-to-one relationship. However, the present disclosure is not limitedthereto. In some embodiments, each decision rule 302 and treatmentguidance 308 have a one-to-many relationship. Furthermore, in someembodiments decision rules 302 and treatment guidance 308 have amany-to-one relationship. Furthermore, in some embodiments the decisionrule database 234 is updated on a continual (e.g., recurring basis) inorder to provide and incorporate the most up-to-date information.

In some embodiments, the drug class database 310 comprises a listing ofone or more drug classes. These drug classes include, but are notlimited to, a metformin class, a SGLT-2 inhibitor class, a GLP-1receptor agonist class, a DPP-4 inhibitor class, an insulin class, athiazolidinedione class, a Sulfonylureas class, and a Glinides class. Insome embodiments, each class listing includes a listing of specificdrugs for the respective drug (e.g., a listing of each specificmetformin drug available in a market). In some embodiments, the listingof specific drugs includes a dosage of each drug, a manufacturer of eachdrug, an indication if the drug is covered by one or more insuranceproviders, a dosage of each drug, a cost of each drug, an availabilityof each drug, etc. In some embodiments, the drug class database 306comprises a record of literature related to diabetes drug classes and/ormetabolic drug classes. In some embodiments, the record of literatureincludes some or all published academic papers, publically availablepatient data, and the like. Furthermore, in some embodiments the drugclass database 310 is updated on a continual (e.g., recurring basis) inorder to provide and incorporate the most up-to-date information.

Referring to FIG. 3, a communication scheme between various databasesand modules of the diabetes patient guidance device 250 will bedescribed in detail, in accordance with an embodiment of the presentdisclosure. In some embodiments, in accordance with a determination thata type of report is determined (e.g., a diabetes treatment report and/ora metabolic status report), the patient profile database 206 providesinformation (e.g., patient panel 208) related to desired report to thedecision rule database 234. This information provided by the patientprofile database 206 is run against the decision rules 302 of thedecision rules database 302, with each fired decision rule 302up-weighting a particular patient pattern. In some embodiments,pertinent information is provided from the drug class database to thedecision rule database 234 in order to account for such information indetermining a patient pattern. This information includes informationthat is pertain to the respective patient relating to specific drugsand/or drug classes, such as an availability of a specific drug and/ordrug class, or a price of a specific drug. Accordingly, if therespective patient has one or more limitations regarding a particulardrug and/or drug class, these limitations are accounted for indetermining a patient pattern for the respective patient. In accordancewith a determination that a patient pattern is selected for therespective patient (e.g., a first patient pattern; a highest-weightedpatient pattern), the patient pattern is provided to the diabetictreatment guidance module 204. The diabetic treatment guidance module204 selects relevant reporting content to include in the respectivepatient report, which is compiled into an appropriate format (e.g., theformat depicted in FIG. 10).

In some embodiments, one or more of the above identified data elementsor modules of the diabetes patient care guidance device 250 forproviding diabetes patient treatment guidance for a patient are storedin one or more of the previously described memory devices, andcorrespond to a set of instructions for performing a function describedabove. The above-identified data, modules or programs (e.g., sets ofinstructions) need not be implemented as separate software programs,procedures or modules, and thus various subsets of these modules may becombined or otherwise re-arranged in various implementations. Forinstance, in some implementations the decision rules database 234 andthe drug class database 310 are subsumed as a single database (e.g.,information pertaining to drug class and/or specific drug limitations isincluded in respective decision rules 302). In some implementations, thememory 192 and/or 290 optionally stores a subset of the modules and datastructures identified above. Furthermore, in some embodiments, thememory 192 and/or 290 stores additional modules and data structures notdescribed above.

In some embodiments, a diabetes patient care guidance device 250 forproviding diabetes patient treatment guidance for a patient is a smartphone (e.g., an iPHONE), laptop, tablet computer, desktop computer, orother form of electronic device (e.g., a gaming console). In someembodiments, the diabetes patient care guidance device 250 is notmobile. In some embodiments, the diabetes patient care guidance device250 is mobile.

It should be appreciated that the diabetes patient care guidance device250 illustrated in FIG. 2 is only one example of a multifunction devicethat may be used for providing diabetes patient treatment guidance for apatient, and that the diabetes patient care guidance device 250optionally has more or fewer components than shown, optionally combinestwo or more components, or optionally has a different configuration orarrangement of the components. The various components shown in FIG. 2are implemented in hardware, software, firmware, or a combinationthereof, including one or more signal processing and/or applicationspecific integrated circuits.

While the system 48 disclosed in FIG. 1 can work standalone, in someembodiments it can also be linked with electronic medical records toexchange information in any way.

Now that details of a system 48 for providing diabetes patient treatmentguidance for a patient have been disclosed, details regarding a flowchart of processes and features of the system, in accordance with anembodiment of the present disclosure, are disclosed with reference toFIG. 4. In some embodiments, such processes and features of the systemare carried out by the basal/bolus diabetes patient treatment guidancemodule 204 illustrated in FIG. 2.

Block 402. With reference to block 402 of FIG. 4, the goal of diabetestherapy is to match as closely as possible suitable interventions, suchas anti-diabetic agents, with the patient's specific diabetes condition.As illustrated in FIG. 2, a diabetes patient care guidance device 250comprises one or more processors 274 and a memory 192/290. The memorystores instructions that, when executed by the one or more processors,perform a method for providing diabetes patient treatment guidance for apatient.

Blocks 404-408. In the method, a biochemical data set 212 is obtained.The biochemical data set comprises a plurality of test results from asingle blood draw of the patient. In some embodiments, the plurality oftest results comprises at least four measurements from the groupconsisting of high-sensitivity c-reactive protein test results 214,adiponectin level test results 216, proinsulin level test results 218,insulin level test results 220, C-peptide test level results 222, HbA1ctest level results 224, and eGFR level test results 226. In someembodiments, the biochemical data set comprises

Referring briefly to FIG. 5B, 5C, and 5D, an exemplary biochemical dataset is depicted as one or more test results. In some embodiments, thetest results which comprise the biochemical data include a completeblood count (CBC), including a white blood cell count, a red blood cellcount, Hemoglobin, Hematocrit, a mean corpuscular volume (MCV), a meancorpuscular hemoglobin (MCH), a mean corpuscular hemoglobinconcentration (MCHC), a red blood cell distribution width (RDW) aplatelet indicator, as well as an absolute and/or percent based readingfor Neutrophils, Lymphocytes, Monocytes, Eosinophils, and Basophils. Insome embodiments, the test results which comprise the biochemical datainclude a complete metabolic panel (e.g., a 14-part metabolic panel). Insome embodiments, the test results which comprise the biochemical datainclude a liquid panel with non-high-density lipoprotein cholesterol(LP+Non HDL cholesterol) test. In some embodiments, the test resultswhich comprise the biochemical data include a Hemoglobin A1c test. Insome embodiments, the test results which comprise the biochemical datainclude an adiponectin test. In some embodiments, the test results whichcomprise the biochemical data include a C-peptide serum test. In someembodiments, the test results which comprise the biochemical datainclude C-reactive protein cardiac test. In some embodiments, the testresults which comprise the biochemical data include a proinsulin test.In some embodiments, the test results which comprise the biochemicaldata include an insulin test. In some embodiments, the test resultswhich comprise the biochemical data include an analyte determined from avenipuncture draw. In some embodiments, the test results includeadditional quantifiable markers, such as a urinalysis test, acardiovascular test, a kidney function test, etc.

Referring to block 406 of FIG. 4, in some embodiments, the plurality oftest results comprises one, two, three, four, five, six, or sevenmeasurements from the group consisting of high-sensitivity c-reactiveprotein test results 214, adiponectin level test results 216, proinsulinlevel test results 218, insulin level test results 220, C-peptide testlevel results 222, HbA1c test level results 224, and eGFR level testresults 226. In some embodiments, the plurality of test resultscomprises one or more additional metabolic condition analytes of apatient including one or more brain analytes (e.g., ghrelin receptor),one or more adipose tissue analytes (e.g., leptin), one or more gutanalytes (e.g., gastric inhibitor polypeptide (GIP)), one or more betacell or pancreatic analytes (e.g., glucagon, pancreatic beta-cellinsulin release), one or more liver function analytes (e.g., aspartateaminotransferase (AST) to alanine aminotransferase (ALT) ratio,gamma-glutamyltransferase, bile acids, etc.), one or more kidneyanalytes (e.g., cystatin, uric acid production, etc.), and/or one ormore cardiovascular analyses (e.g., blood pressure, troponin, NT-BNP,matrix metallopeptidase 9 (MMP-9), etc.). Accordingly, in someembodiments the plurality of test results includes one, two, three, for,five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen,fifteen, or sixteen measures from the above described analytes and testresults. In some embodiments, each test result and/or analyte is amulti-dimensional condition, such that the test results describe morethan one condition or dimension of a patient. For instance, acardiovascular analyte of a blood pressure reading is consideredmultidimensional since blood pressure readings include a systolic bloodpressure and a diastolic blood pressure. Accordingly, information can beinferred from the overall blood pressure, the systolic blood pressure,and/or the diastolic blood pressure of the patient. As anothernon-limiting example, in some embodiments beta-cell secretion is amulti-dimensional condition as the condition includes insulin secretionand C-peptide secretion. As the medical industry continues to evolve andprogress, additional anatyes may be determined to be particularly usefulin diabetes treatment, and thus included in the present disclosure.Thus, recording these analytes may be of use for implementations infuture patient patterns and reports. Furthermore, in someimplementations a physician requests a particular analyte to bedetermined or test to be conducted, which is included within thebiochemical data set of the respective patient.

Referring to block 408 of FIG. 4, in some embodiments the plurality oftest results consists of high-sensitivity c-reactive protein testresults 214, adiponectin level test results 216, proinsulin level testresults 218, insulin level test results 220, C-peptide test levelresults 222, HbA1c test level results 224, and eGFR level test results226.

By way of non-limiting example, a C-peptide test measures the level ofC-peptide test in the blood. It is generally found in amountsproportional to insulin because insulin and C-peptide are linked whenfirst made by the pancreas. A C-peptide test can be done when diabeteshas just been found and it is not clear whether type 1 diabetes or type2 diabetes is present. A person whose pancreas does not make any insulin(type 1 diabetes) has a low level of insulin and C-peptide. A personwith type 2 diabetes can have a normal or high level of C-peptide. AC-peptide test can also help find the cause of low blood sugar(hypoglycemia), such as excessive use of medicine to treat diabetes or anoncancerous growth (tumor) in the pancreas (insulinoma). Becauseman-made (synthetic) insulin does not have C-peptide, a person with alow blood sugar level from taking too much insulin will have a lowC-peptide level but a high level of insulin. An insulinoma causes thepancreas to release too much insulin, which causes blood sugar levels todrop (hypoglycemia). A person with an insulinoma will have a high levelof C-peptide in the blood when they have a high level of insulin.

By way of another non-limiting example, the high-sensitivity CRP(hs-CRP) test is used to check for cardiovascular disease. In someinstances, hs-CRP level of lower than 1.0mg/L indicated low risk of CVD(heart disease), hs-CRP level of 1.0 mg/L and 3.0 mg/L means a moderaterisk of CVD, and hs-CRP level of more than 3.0 mg/L indicates a highrisk of CVD.

Blocks 410-412. Referring to blocks 410 and 412 of FIG. 4, the methodfurther includes obtaining a demographic data set 228 for the patient,the demographic data set comprising (i) an indication of a gender 230 ofthe patient (e.g., patient) 207 and/or (ii) an enumerated indication ofa diabetes stage and/or stage of treatment therapy 232 of the patient207. In some embodiments, the enumerated indication of a diabetes stageof the patient is one of (i) diagnosed as pre-diabetes, (ii) diagnosedwith diabetes but not taking a drug (iii) diagnosed with diabetes andtaking a first line diabetes drug (iv) diagnosed with diabetes andprescribed multiple diabetes drugs without insulin and (v) diagnosedwith diabetes and prescribed multiple diabetes drugs with insulin. Insome embodiments, the enumerated indication of a diabetes stage of thepatient includes one or more subdivisions of the above described stages(e.g., a subdivision of a diabetic stage and/or a subdivision of atreatment therapy), such as a subdivision that includes patientsdiagnosed with diabetes and only taking metformin. In some embodiments,the demographic data set includes biometric information of therespective patient including a height of the patient, a weight of thepatient, an age of the patient, etc.

Block 414. Referring to block 414 of FIG. 4, the method further includesusing the biochemical data set 212 and demographic data 228 set to mapthe patient onto a first patient pattern in a plurality of patientpatterns. In some embodiments, this comprises running the biochemicaldata set 212 and demographic data 228 against a series of decision rules302 in order to identify a drug class order. In some embodiments, theseries of decisions rules 302 include each decision rule stored in thedecision rule database 234. In some embodiments, the series of decisionrules 302 include a subset selected from all of the decision rulesstored in the decision rule database 234. For instance, in someembodiments depending on information provided by the biochemical dataset 212 and the demographic data 228, one or more decision rules 302 isnot application (e.g., a decision rule dedicated to a metabolic statusof a patient is not utilized in generating a diabetes treatment guidancereport). As illustrated in FIG. 2B, a decision rule 302 includes anumber of firing conditions 306 and one or more associated treatmentguidance 308 that is provided if this decision rule is fired. In someembodiments, firing of a respective decision rule up-weights arespective treatment guidance 308 and/or patient pattern. Accordingly,in some embodiments if more than one decision rule is fired, therespective treatment guidance 308 and/or patient pattern that isup-weighted the most is determined to be the most relevant guidanceand/or pattern (e.g., a first patient pattern). However, the presentdisclosure is not limited thereto. For instance, in some embodimentsonly one decision rule 302 is fired as each decision rule encompasses aunique set of firing conditions that differentiate a first decision rulefrom a second decision rule. FIG. 8 provides a partial example of suchdecision rules 302. For example, drawing upon biochemical data set 212and demographic data 228 set, the firing conditions 306 of decision rule302-1 specify that insulin resistance is severe (condition 306-1),(3-cell stress is significant (condition 306-2), and cardiovascular riskis high (306-3). If these firing conditions are satisfied, associatedtreatment guidance 308 in the form of a drug class order proposal isprovided. In typical embodiments only a single drug class order isprovided. However, the present disclosure is not limited thereto. Forinstance, in some embodiments, two drug class orders are provided, threedrug class orders are provided, four drug class orders are provided,five drug class orders are provided, six, drug class orders areprovided, seven drug class orders are provided, eight drug class ordersare provided, nine drug class orders are provided, ten drug class ordersare provided, eleven drug class orders are provided, or twelve drugclass orders are provided. In some embodiments, the number of drug classorders that are provided is not limited to a particular number, suchthat each suitable (e.g., relevant) drug class order is provided for aphysician to consider. In FIG. 8, and throughout the present disclosure,M=metformin, S=SGLT-2 inhibitors, G=GLP-1 receptor agonists, D=DPP-4inhibitors, I=insulin, T=thiazolidinediones, and F=Sulfonylureas, andthe position of this latter class also is used for Glinides.

Several steps are involved in training the firing conditions 306 andassociated treatment guidance 308 for each decision rule 302. First,suitable firing conditions 306 need to be identified. This involveselucidating the physiological pathway underlying patient patterns. Thiswork includes taking biochemical data sets 212 from a number ofpatients, decomposing the data, sometimes down to one element of thedata (e.g. HbA1c levels), sometimes down to three or four interrelatedelements of the data, and combining such data, for example, to form andscale suitable patient classifications (e.g., that this aspect of apatient is mild and that one is severe and the next moderate, etc.).FIGS. 6 and 7 illustrate. As illustrated in FIG. 6, diet and exerciselowers patient fasting glucose, improves patient oral glucose tolerancetest results, lowers HbA1C levels, lowers proinsulin levels, lowersC-peptide levels, lowers insulin levels, raises adiponectin levels, andlowers high sensitivity CRP levels. On the other hand, drugs in thesulfonylurea/glinide classes lower patient fasting glucose, improvepatient oral glucose tolerance test results, lower HbA1C levels, butraise proinsulin levels, raise C-peptide levels, raise insulin levels,and have a marginal effect on adiponectin levels and high sensitivityCRP levels. As such, this informs the basis for making decisions rulesthat includes in the patient's treatment guidance 308 advice regardingorder of precedence of sulfonylurea/glinide classes of drugs. As such,once suitable patient classifications that track observed physiologicalpathway and underlying patient patterns have been identified and scaled,the linkage step of forming decision rules 302 for the doctors that linkfiring conditions (e.g., classifications in the form of scaled ranges ofparticular patient data) 306 to diabetes patient treatment guidance 308(e.g., recommended drug profiles that specify an order of precedence ofdrug classes or other forms of intervention, including, in someinstances, recommendations on disfavoring certain drug classes for aparticular set of firing conditions). As such, the diabetes patienttreatment guidance 308 associated with a given decision rule 302, in theform of a drug profile (order of precedence of drug therapies), is acombination of the arrow chart data provided in FIGS. 6 and 7. Forinstance, the arrow chart data provided in FIGS. 6 and 7 allows for oneto predict patient outcomes under several different “what-if” scenariosin which a patient with, for example, no drug on board, given theirpanel 208 comprising a biochemical data set 212 and demographic data set228, and then add the drug classes. For instance, how much somehypothetical therapy combination would affect levels of markers measuredin the biochemical data set 212. Moreover, FIGS. 6 and 7 illustrate notonly whether a given drug class affects a marker present in thebiochemical data set 212, it also provides, based on size of the arrow amagnitude of the effect. In some embodiments, the magnitude of theeffect is depicted as a color gradation (e.g., red has a severe effect,yellow has a mild effect, green has a no effect). In this way, FIGS. 6and 7 look, from a drug naive state for each particular drug class, atwhat changes to the relevant markers 214 through 226 in the biochemicaldata set 212 are achieved. This information in FIGS. 6 and 7 providesgeneral guidance about how certain drug classes affect key markers 214through 226 in the biochemical data set 212 but not quite at the samedetailed level of being able to exactly predict what can be expect fromthe impact on a particular pattern, because the data in FIGS. 6 and 7represent the marker delta of going from no drug to having drug onboard. As such, it is the linkage between each drug and its effect onthe markers that calls for physician judgment between the profile of apatient given the marker values 214 through 226 of a given biochemicaldata set and markers 230 and 232 of a demographic data set from a panel208 of a patient 207 and the characteristics of each drug class used totreat diabetes patients. Importantly, the clinical judgment fromadvisory physicians serves an important role because the multi-factorialnature of the decision on ordering drug classes varies widely over therange of possible patient profiles, and is further frequently changing,so the ordering decision is extraordinarily difficult to mathematicallymodel in a way that would be generally accepted, and applicable to allplausible patient biochemical data and/or demographic data profiles,particularly over time. Advantageously, the disclosed systems andmethods of the present disclosure are set up to accommodate thisdynamic. Further, the disclosed systems and methods make use of numerousreference articles and a panel of advisory physicians that have assessedsuch data, and other data, in order to develop decision rules forspecific sets of trigger conditions, where each such decision rule 302provides a drug class order, among other forms of diabetes patienttreatment guidance, for a given set of marker values in the biochemicaldata set 212 and demographic data set 228.

Referring to FIG. 8, in some embodiments, the diabetes patient treatmentguidance 308 includes a drug class order. Each drug class order providesinterpretation to preferably use one drug class before another becauseit would have more impact given the profile of the marker values of thepatient (e.g., elements 214 through 226) in the biochemical data set 212and marker values (e.g. elements 230 and 232) in the demographic dataset 228 for a given patient. Thus, if the diabetes patient treatmentguidance 308 specifies the drug class order (M, S, G), then metformins(M) are deemed to have more beneficial impact then SGLT-2 inhibitors (S)which in turned are deemed to have more beneficial impact than GLP-1receptor agonists (G). In other words, if the diabetes patient treatmentguidance 308 of a given decision rule 302 has placed treatment Class Aahead of treatment Class B, then, in accordance with the systems andmethods of the present disclosure, based on the medical judgment ofexperts consulted in developing the disclosed invention, drug Class A isdeemed to have a better overall impact on patient condition given themarker values in a panel 208 comprising the biochemical data set 212 anddemographic data set 228 of that patient. As such, decision rules 302provide information on how a given drug class effects a given patientcondition by uniquely linking marker values in the biochemical data set212 and demographic data set to patient outcome upon treatment with thegiven drug class. As such the disclosed decision rules 302 provideinformation on both ends of the linkage (marker to patient guidance)based upon expert consensus regarding which drug class is better for agiven set of patient marker values (e.g., elements 214 through 226 andelements 230 and 232 of FIG. 2). In so doing the expert consensus makesinformed judgments about what is important in the profile (e.g., valuesfor elements 214 through 226 as well as elements 230 and 232 of FIG. 2)as those elements relate to the likelihood of efficacy and optimizationof drug therapy.

In the literature, as new drugs and drug classes to treat diabetes areintroduced, they are backed by a substantial amount of publishedcomparative information that compares these new newer drugs and drugclasses to very old ones. As such, the published literature rarelycompares a new drug entrant against another new one. Moreover, ratherthan establish that the new drug is more efficacious than existingdrugs, the trend for introducing a new drug to market has been, and is,to simply establish that the new drug is not inferior to existing drugs.That is, the new drug is as good as the comparator drug that is alreadybeen approved by a regulatory agency such as the United States Food andDrug Administration. As such, published studies for new drugs that aresponsored by their manufactures are geared to establish non-inferiorityin order to support drug approval, as opposed to tackling the morecomplex task of establishing which drug is best to improve a diabeticcondition. While such information is helpful to the drug company seekingdrug approval, such studies are inadequate for assisting a clinician(e.g., physician, nurse practitioner, or similar health professional) intrying to decide which drug or drug class is best for a given diabetespatient's condition. This is particularly the case in many instanceswhere such new drug studies are deliberately designed to generate datathat will show that they have the same efficacy as another existing,approved drug. As such, publications concerning the efficacy of newdrugs are based on an overall objective (not inferior to, just asefficacious as) that is the exact opposite of generating informationthat would help a physician to make a difficult decision on which drugsor drug classes to select from for a given patient.

One approach to forming the decision rules 302 is to evaluate eachliterature and/or non-published information for each respective drugclass. Evaluating literature as it is produced allows for the mostrecent quantification of how the respective drug class will change thevalues of markers in the biochemical dataset 212. In some embodiments,such evaluation needs to also consider how the values of markers, orcombination of markers, in the biochemical dataset 212 will change giventhe values of the markers in the demographic dataset 223. In someembodiments, the evaluation is conducted by a plurality of expertphysicians. Accordingly, in some embodiments each piece of literature isevaluated by a subset of the expert physicians. In so doing, decisionrules 302 are formulated and the assay results from the patient, in theform of the biochemical data set 212 and the demographic data set 228from the patient are then used to find the decision rule that bestmatches the patient condition and thus has a maximal beneficial impacton the patient.

As such, a central tenet of some embodiments of the systems and methodsof the present disclosure is that the physician submitting thebiochemical data set 212 and demographic data set in block 410 foranalysis by the diabetes patient treatment guidance module 204 inaccordance with block 414 indicate the stage of disease or therapy 232of the patient. One reason for this is that diabetes has discretestages, and different drugs, or drug combinations, work better atdifferent stages of the disease. For example, the early stages, when apatient is prediabetes, or they are newly diagnosed, and they still havenot taken a drug for diabetes, defines an essential stage of disease.Stages beyond this initial stage, once the patient is already beingtreated, are difficult to ascertain because in a sense, it is notreadily possible to distinguish from the marker values 214 through 226of the biochemical data set the stage the disease is in because they areunder treatment. In such instances the drugs the patient is taking havevarious effects on the values of these markers. As a consequence, assoon as you put the drug in the mix of factors to consider whendeveloping diabetes patient treatment guidance, it obscures what is theunderlying condition versus what is the impact of the drug.

To address such situations, the systems and methods of the presentdisclosure rely upon multiple firing conditions 302 for each decisionrule 308. For example, consider the case where patient 1 is simplytaking their very first drug, and they are on a pill, which is typical,usually that is a first line of treatment. And such first line drugstend to have fairly narrow action, primarily controlling sugar, notreally addressing much of anything on the underlying level regarding thediabetic condition. That patient is in a completely different situationwith respect to what treatment options might make sense next, versus asecond patient who is already on two drugs where one is insulin and oneis a multi-action pill, and there is already a substantial amount ofdifferent effects from the two drugs that are on board. Determining thediabetes patient treatment guidance for the second patient requirestaking into account that context (e.g., using firing conditions 306)that they are already taking two drugs that do a lot to change markervalues in the biochemical data set 212 of that second patient. Thus,analysis of the second patient's biochemical data set 212, given theirdiabetes stage/therapy of the patient 232 (that their biochemical dataset 212 is impacted by the influence of two drugs they are currentlytaking, results in quite a different course of action (diabetes patienttreatment guidance 308) for the second patient than the first patienteven if they have similar marker values in their respective biochemicaldata sets 212. As such, the biochemical data set 212 of the first andsecond patient is interpreted differently. That is, the diabetes patienttreatment guidance is going to try to move the values of the markers inthe patient's biochemical data set 212 differently with the intervention(diabetes patient treatment guidance 308) and thus move the underlyingpattern function differently for these two patients.

In this regard, the demonstration of the magnitude of various forms ofintervention on fasting glucose levels, the OGTT test, HbA1C levels,proinsulin levels, C-peptide levels, insulin levels, adiponectin levels,and hs-CRP levels illustrated in FIGS. 6 and 7, in response to variousforms of intervention, is drawn for a patient that has not yet undergonedrug treatment. The effect of each drug class on the markers will bedifferent for those patients that have undergone some form of treatment.Accordingly, different decision rules, with firing conditions anddifferent diabetes treatment guidance to match such patients are drawnthan the decision rules that are drawn to pre-administration baselinepatients. The effects of each drug will have a difference in magnitudefor post-administration patients than illustrated in FIGS. 6 and 7.However, the responses will not be directionally different. That is, aneffect of a drug would not change from being beneficial for a marker tobeing adverse for a marker, but there would be significant differencesin the magnitude of the impact on the marker.

In FIGS. 6 and 7, a first marker represented by a larger arrow undergoesa stronger effect than a second marker represented by a smaller arrowwith the proviso that the effect on both markers will deviate inmagnitude depending on the starting treatment condition of the patient.In fact, there are substantial differences in the impact of a markervalue that can be realized depending on their current treatmentcondition/diabetes stage 232. For example, consider the case of apatient already on insulin and three drugs total, meaning they aretaking two additional medicines beyond insulin. The patient may havevery little β-cell function left at all at that point. β-cell are uniquecells in the pancreas that produce, store and release the hormoneinsulin. So even if a drug was prescribed to affect the β-cells as muchas we possible, there is just not much response left and so thedirectional change associated with the drug for the β-cells remainsconsistent with a pre-administration baseline, but the magnitude will bemuch smaller than that of the pre-administration baseline/pre-diabetesor early stage diabetes patient. On the other hand, consider the case ofsomeone who is on a single pill early in the disease where they have noteven adequately addressed a particular pattern. Adding another drug forpatients in this instance could have a really big effect, because thereis a lot of latent capacity that is substantially not being used.

As such, the systems and methods of the present disclosure takequantitatively into account these changes in magnitude, depending on thestage of therapy on a category by category basis. That is, a deliberateattempt is made to not be overly precise about the exact marker numbersand more precise about multiple dimensions in the profile. In otherwords, more emphasis is placed on determining for a given patient wheninsulin resistance is simply “significant,” or is “really severe,” thatis categorical classification into discrete ranges as a basis forestablishing firing conditions 306, versus establishing first conditionsas a function of a continuous range: e.g., trying to establish a firingcondition as a function of whether a patient's proinsulin value is 14versus, 14.5, versus 15.0 versus 16.

In typical instances, diabetes patient treatment guidance 308 identifiesdrug classes rather than specific drugs within a class. This allows aphysician to choose a specific drug within a recommended drug class. Itmay be the physician has more experience with dosing one drug in theclass than another. Moreover, the diabetes patient treatment guidance isagnostic to drugs within a given class in recognition of other factorsas well, including the real world limitation today on formularyavailability. For instance, often a particular brand is simply notavailable for a particular case, because of an administrative decisionunrelated to the practice of medicine. For instance, certain companies'drugs being listed as preferred providers, some drugs not being coveredby insurance companies because there is a generic equivalent, etc. Inthis way, the diabetes patient treatment guidance circumvents brandbeliefs, the cumulative impact of marketing, prescribing physician'sbeliefs about particular drugs in a class, etc. By limiting diabetespatient treatment guidance to the drug class level, as opposed torecommending certain drugs within a class over drugs in the same class,what the systems and methods of the present disclosure provide, using ananalogy to cars, is information about what drug class is a sports car,what drug class is a station wagon, what drug class is a pickup truck.If the systems and methods of the present disclosure were to try toprovide diabetes patient treatment guidance that discriminates betweendrugs in the same drug class, again using the analogy to cars, thiswould be arguing at the level that a Chrysler pickup is better than aFord pickup which is better than a GM pickup, which is a situation to beavoided because it is subjective in view of the lack of suitableclinical trials to prove such relationships and because of non-medicalpolicies that affect formulation availability.

In this way, further, the systems and methods of the present disclosurefocus on major decisions, such as whether to treat the β-cells or not.That is, to identify which treatment objectives are major objectives,which treatment objectives are minor, and which treatment objectiveswill not even be addressed at all in the diabetes patient treatmentguidance of a given patient.

Block 418. Referring to block 418 of FIG. 4, the method further includespreparing a report based on an identity of the first patient pattern. Anexample of one such report is provided in FIGS. 10A through 10G for aparticular patient that has diabetes and is presently taking multipledrugs without insulin. The report provides a rank ordering ofintervention classes among a set of preferred intervention classes basedon the identity of the first patient pattern. The report collectivelyillustrated across FIGS. 10A through 10G is one of a plurality of suchreports listed in example FIG. 9, each for a different set of markervalues, or more precisely, range of marker values in the biochemicaldata set 212 and demographic data set 228. Reports may vary over time incontent, number of pages, amount of detail, and the like once they arein the market, depending on customer needs.

Each such report is a combination of assessments made using the systemsand methods of the present disclosure as well as a summary ofinformation relevant to the patient condition from the literature. Thereport allows a clinician to make real world determination such as theeffect that will be realized for a given patient from a given drugclass, and then based on such information, how this relates to thepatient's profile (e.g., biochemical data set 212). The reports provideample room for medical judgment in how the profile is weighted. Forexample, consider the case where the patient profile (e.g., biochemicaldata set 212) shows the patient has severe cardiovascular inflammationindicating a lot of risk for life-altering things like strokes, and alot of insulin resistance. In such an instance the report may advisethat the treating physician should really focus on those two things.Both of those are significant risk drivers over the course of the nextfew years of this person's life. An individual physician might decidefor whatever reason they don't think that is important. That is why thereport supplies as much detail as illustrated in FIGS. 10A through 10J.All the raw data of the tests (e.g., the biochemical data set 212 markervalues and the demographic data set 228 marker values), and each drugclass is considered. Sometimes, for good reason, sometimes not for goodreason, probably, an individual clinician may disagree with some aspectof the report. However, even in such instances the individual clinicianhas enough information, in the report, to look at the situation andactively think about the questions—e.g., is drug class A better thandrug class B better than drug class C for this individual patient (giventheir biochemical data set 212 marker values and the demographic dataset 228 marker values), and are there reasons with this patient thatmaybe I would do something different? For instance, perhaps the patientalready indicated that they are quite willing to start up significantcardiovascular treatment. Perhaps there is relevant family historyinformation, such as the patient's uncle recently died of a stroke,etc., which causes a patient and their treating clinician to be moreaggressive (or less aggressive) than recommended by the report. So insuch a case, the doctor might then go off in a slightly differenttreatment direction for other aspects of the diabetes condition, beyondthe cardiovascular condition, then recommended in the report, tocompensate for the fact that they are already treating thecardiovascular condition more aggressively than recommended in thereport. At the same time, there is sufficient information that theclinician can assess the trade-offs involved in taking a differentcourse, rather than follow the order of those suggested by the report.

Referring to FIG. 10, an exemplary patient report is provided. In someembodiments, the patient report includes information for a medicalpractitioner (e.g., the patient reports of FIGS. 10A through 10J). Insome embodiments, the patient report includes information for thepatient (e.g., the patient reports of FIGS. 10K and 10L). In someembodiments, each patient report includes one or more sections. In someembodiments, a subset of the one or more sections is staticallygenerated to include predetermined information, such as a predeterminedset of biochemical data set values. In some embodiments, a subset of theone or more sections is generated from information stored in the one ormore databases and/or modules of the diabetic patient care guidancedevice 250. In some embodiments, a subset of the one or more sections isone or more rules (e.g., decision rules 302 of FIG. 3), that if firedtrigger particular information to be included in a respective section(e.g., a rule is fired if a patient has severe insulin resistance whichprovides a corresponding section on insulin resistance in the report).

Referring briefly to FIGS. 10A through 10J, a report that includesinformation for a medical practitioner is depicted. In some embodiments,the one or more sections include a patient panel test result sectionthat includes information related to particular test results and/orbiochemical data set values of the patient. For instance, referringbriefly to FIG. 10A, a patient panel test result section for a medicalpractitioner is depicted. In some embodiments, the one or more sectionsinclude a test result interpretation section that includes informationrelated to interpreting the results of the patient panel test and/orother auxiliary information inferred from test results and/orbiochemical data set values. In some embodiments, the one or moresections include a current drug recommendations section that providesone or more treatment recommendations. In some embodiments, the one ormore sections include a patient current condition section that includesinformation related to a patient's current stage and progression relatedto one or more medical conditions. In some embodiments, the one or moresections include a patient goal section that includes informationrelated to one or more goals of the patient, including personal patientgoals and/or biochemical data set value goals. In some embodiments, theone or more sections include a physician course of action section thatincludes information related to aiding an expert physician indetermining a treatment guidance plan. In some embodiments, the one ormore sections include a detailed drug recommendation section thatincludes information related to one or more specific drugs and/or drugclasses including a prioritization thereof In some embodiments, the oneor more sections include a treatment guidance adjustment section thatprovides recommended adjustments for a medical condition and/ortreatment guidance plan. In some embodiments, the one or more sectionsinclude a reference section that includes one or more cited referencesused in forming the respective report.

Referring briefly to FIG. 10K and 10L, a report that includesinformation for a patient is depicted. In some embodiments, the reportincludes information related to the one or more above describe sectionsthat is subsumed in a single section. This single section includesinformation that is not difficult for an uneducated patient to consume,such as an interpretation of one or more test results and/or biochemicaldata set values specifically for the patient.

Referring to FIG. 14, in some embodiments, the report provides a graphicrepresentation of one or more markers of a patient in relation toanticipated values for their stage of diabetes. This representation isuseful as often an analysis of only numerical values can lead tomisdiagnosis. For instance, as illustrated in FIG. 14, values forbeta-cell secretion are within a common range for stage I and stage IIof type 2 diabetes. Without including the insulin resistance values inthe graphical representation, a physical would have difficultydetermining which stage a patient is in, since many of the values aloneprovide inconclusive information.

As such there can be nuances on how the report is used. However, oneobjective of the systems and methods of the represent disclosure is thatthe report provided in accordance with block 418 have enough informationthat a clinician can make decisions that deviate from the diabetespatient treatment guidance 308. This is in recognition that there aresubtleties in judgment made by the physicians when taking into accountthe entire profile (biochemical data set 212 and demographic dataset228) in developing the disclosed decision rules 304 that, when looked atby someone else, may indicate a reason(s) to deviate. Moreover, thetreating physician has to contend with real world obstacles rather thanthe abstract, such as when the best drug would be an injection, and thepatient refuses injections, and the like. In such situation, these realworld obstacles necessitate the treating physician to make an adjustmentto the diabetes patient treatment guidance in the report 308, and oncethey make one such adjustment, they may need to make other ancillaryadjustments to the diabetes patient treatment guidance 308 in the reportto compensate for the first adjustment. As such, these factors, that areat times of a non-medical nature (e.g., refusal to accept injections,etc.) and may be more related to patient preference, combined with thesubstantial information regarding the patient's condition, all coupledwith the goal of seeking to address the major treatment decisions (e.g.,which drug class to take as opposed to which drug within a class totake), may require adjustments to the recommended treatment regimens. Asimple outline of the most important treatment goals given a patientprofile enables a treating physician to adapt the report to a patient'sspecific needs, both medical and non-medical.

As such, an important strength of the disclosed systems methods is thereliance on treating physician judgment given that the diabetescondition arises from such a complex set of factors. In short, weighingall the factors, both medical and non-medical, that could potentiallyaffect the decision rules would lend itself to too many dimensions.Rather, the disclosed systems and methods rely on, in preferredembodiments the disclosed seven biochemical analytes (markers 214, 216,218, 220, 222, 224, and 226, plus the patient's gender 230 and diabetesstage/therapy stage of the patient 232 to construct a multi-dimensionalprofile of the condition of the patient in a clinical context, and atthe same time cover a limited number of important dimensions relevant totreatment selection. Thus, a report may be provided as to majorobjectives, which coupled with the treating physician's clinicalperspective results in a treatment plan in which no major objectives aremissed and the best impact is realized for a given patient panel.

As such the report in accordance with block 418 is written to be apartial aid for the decision. As such, the report provides helpfuladvice even though some embodiments of the disclosed systems and methodsdoes not take into account a whole array of factors, such as whatmedications the patient's formulary will permit, whether the patient hasany insurance coverage issues, what medications the patient is willingto take, patient beliefs (e.g., refuses to take a certain drug becausefriend/relative took the same drug and had bad side effects, etc.).These factors are generally considered to be a part of the art ofmedicine, since they cannot be assessed quantitatively, and thusincluded in a report. The reports in accordance with block 418 arewritten at sufficiently high level that the treating physician can takeinto account these unaddressed factors while still making sure that themajor treatment objectives outlined by the report are addressed or atleast considered. As such, the report effectively brings the treatingphysician to the point where they have an appreciation of the presentbiochemistry of their patient and, given this present biochemistry,which treatment objectives should be pursued nor not pursued. Thetreating physician can then adapt the report to specific drugs inrecommended drug classes, or make informed decisions in forming atreatment plan that deviates from the report in order to address thefactors, such as patient preference, formulary availability, etc., thatthe report does not attempt, and arguably should not attempt, toaddress. Thus, this limited goal of the present disclosure is asubstantial contribution in the treatment of the diabetes condition.

As the above disclosure indicates, the stage of therapy informs theclinician about what is failing in the patient, for example the β-cells,etc. As another example, there is a class of drugs called sulfonylureasand a percentage of patients can do pretty well on those depending ontheir β-cell function. If a patient retains a large β-cell population,e.g. their pancreas is very resilient, they can keep on favorablyresponding to the sulfonylureas for a long time. The β-cell functionmarkers provide some insight into this resilience. For instance, if themarkers for β-cell function remain good for a particular patient, thensulfonylurea treatment can continue. On the other hand, if someone is onthat drug class and their β-cells are essentially exhausted, the markersin the biochemical data set 212 would indicate this, and there is nopoint in continuing the sulfonylurea treatment to stimulate β-cells torelease insulin. In such instances, for sulfonylureas for such apatient, the report in accordance with block 414 would say “notrecommended” because it is not going to have any beneficial impact onthe patient's condition.

As such another aspect that the reports in accordance with block 418provide is information not only about what drug classes to consider butalso which ones to not even bother with or those drug classes that areintermediate in likely efficacy, and as such are possible treatments.For example, sometimes there can be reasons for such treatment, or tocontinue such treatment if it is already underway. For example, thepatient may already be on a drug, there may be cost issues, that giverise to diabetes patient treatment guidance 308 to the effect that whilea certain drug class that the patient is currently taking is not on theoptimal list for the particular patient profile, but because they arealready on it and they are used to it the diabetes patient treatmentguidance 308 notes the advantage of continuing on with the drug (or drugclass) at that point from just a simplistic compliance point of view.The patient is used to it. This would include a drug class that, iftreatment were starting de novo, wouldn't have been prescribed to thepatient. But since it has been prescribed, a report in accordance withblock 418 may provide intermediate support for continuing use of thedrug.

To be clear, in preferred embodiments of the systems and methods of thepresent disclosure, specific drugs that the patient is currently takingare not provided as part of the demographic data set 228. Rather, insome embodiments, the diabetes stage/therapy stage of the patientrequires that each patient be classified into one of five categories,either they are (i) diagnosed as pre-diabetes, (ii) diagnosed withdiabetes but not taking a drug (e.g., they may have a fairly mild casewhere the doctor prescribed exercise and diet changes, for a period oftime such as a year, and it didn't really work and so that patient has adiabetes diagnoses but no drug yet), (iii) the patient has beenprescribed a first line drug meaning that the patient is on one drug,(iv) the patient has prescribed multiple drugs without insulin and (v)the patient has been prescribed multiple drugs with insulin. Suchembodiments of the present disclosure do not discriminate betweensituations in which a patient is taking two or three kinds of diabetesdrugs because it doesn't make a lot of difference. The realdifferentiator is whether the patient is taking insulin because that isa direct effect to basically replace the pancreatic production ofinsulin. As such, there are quite a few cases where the situation of thepatient's disease state in therapy 232 combined with the marker data inthe biochemical data set 212 will lead down a path to say that aparticular drug class is not recommended for a given patient or that itis possible that there are others drug classes that are better.

REFERENCES CITED AND ALTERNATIVE EMBODIMENTS

All references cited herein are incorporated herein by reference intheir entirety and for all purposes to the same extent as if eachindividual publication or patent or patent application was specificallyand individually indicated to be incorporated by reference in itsentirety for all purposes.

The present invention can be implemented as a computer program productthat comprises a computer program mechanism embedded in a non-transitorycomputer readable storage medium. For instance, the computer programproduct could contain the program modules shown in any combination ofFIGS. 1, 2, 3, 5 and/or described in FIG. 4. These program modules canbe stored on a CD-ROM, DVD, magnetic disk storage product, USB key, orany other non-transitory computer readable data or program storageproduct.

Many modifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only. The embodiments were chosen anddescribed in order to best explain the principles of the invention andits practical applications, to thereby enable others skilled in the artto best utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. Theinvention is to be limited only by the terms of the appended claims,along with the full scope of equivalents to which such claims areentitled.

1-20. (canceled)
 21. A method for providing a healthcare professionalwith a metabolic status of a patient that is being tested or has beentested for a diabetic or pre-diabetic condition, the method comprising:obtaining a biochemical data set, via a computer system having aprocessor programmed to obtain the biochemical dataset, wherein thebiochemical data set comprises a plurality of test results from a singleblood draw of the patient, wherein the plurality of test resultscomprises at least three measurements from the group consisting of ahigh-sensitivity c-reactive protein test, an adiponectin level test, aproinsulin level test, an insulin level test, a C-peptide test, a HbA1ctest, and an eGFR level test; obtaining a demographic data set for thepatient, via a computer system having a processor programmed to obtainthe biochemical dataset, wherein the demographic data set comprises (i)an indication of a gender of the patient and (ii) an enumeratedindication of a patient's stage of disease or a current therapy, whereinthe enumerated indication of the patient's stage of disease or thecurrent therapy is one of (a) diagnosed as pre-diabetes, (b) diagnosedwith diabetes but not taking a drug (c) diagnosed with diabetes andtaking a first line diabetes drug (d) diagnosed with diabetes andprescribed multiple diabetes drugs without insulin and (e) diagnosedwith diabetes and prescribed multiple diabetes drugs with insulin, or(f) a current therapy for the diabetic or pre-diabetic condition;running all or a portion of the biochemical data set and the demographicdata set against a plurality of decision rules via a computer systemhaving a processor programmed to perform the running, wherein, inaccordance with a determination that one or more firing conditions ofeach respective decision rule in the plurality of decision rules isfired, a patient pattern is selected, from among a set of at least20,000 patient patterns, through the comparison of (i) the determinationthat one or more firing conditions of each respective decision rule inthe plurality of decision rules is fired to (ii) each patient pattern inthe set of at least 20,000 patient patterns, wherein the patient patterncomprises a pattern of insulin resistance, β-cell stress level, andcardiovascular inflammation; and preparing a report based on an identityof the patient pattern, via a computer system having a processorprogrammed to perform the preparing, wherein the report provides ametabolic status of the patient.
 22. The method of claim 21, wherein theplurality of test results comprises three measurements selected from thegroup consisting of a high-sensitivity c-reactive protein test, anadiponectin level test, a proinsulin level test, an insulin level test,a C-peptide test, an HbA1c test, and an eGFR test.
 23. The method ofclaim 21, wherein the plurality of test results comprises fourmeasurements selected from the group consisting of a high-sensitivityc-reactive protein test, an adiponectin level test, a proinsulin leveltest, an insulin level test, a C-peptide test, an HbA1c test, and aneGFR test.
 24. The method of claim 21, wherein the plurality of testresults comprises five measurements selected from the group consistingof a high-sensitivity c-reactive protein test, an adiponectin leveltest, a proinsulin level test, an insulin level test, a C-peptide test,an HbA1c test, and an eGFR test.
 25. The method of claim 21, wherein theplurality of test results comprises six measurements selected from thegroup consisting of a high-sensitivity c-reactive protein test, anadiponectin level test, a proinsulin level test, an insulin level test,a C-peptide test, an HbA1c test, and an eGFR test.
 26. The method ofclaim 21, wherein the plurality of test results comprises sevenmeasurements selected from the group consisting of a high-sensitivityc-reactive protein test, an adiponectin level test, a proinsulin leveltest, an insulin level test, a C-peptide test, an HbA1c test, and aneGFR test.
 27. The method of claim 21, wherein the plurality of testresults consists of measurements from a high-sensitivity c-reactiveprotein test, an adiponectin level test, a proinsulin level test, aninsulin level test, a C-peptide test, an HbA1c test, and an eGFR test.28. The method of claim 21, wherein one or more decision rules in theplurality of decision rules is determined according to an analysis ofthe biochemical data set and the demographic data of each patient acrossa cohort population of patients conducted by a plurality of expertphysicians.
 29. The method of claim 21, wherein one or more decisionrules in the plurality of decision rules is determined according to ananalysis of at least a peer reviewed reference pertaining to a drugclass for treatment of diabetes.
 30. The method of claim 21, whereineach firing condition in the one or more firing conditions of arespective decision rule in the plurality of decision rules includes oneor more conditions selected from the group consisting of a diabetesstage of the patient, a number of medications currently being taken bythe patient, a dosage of a medication currently being taken by thepatient, a type of medication currently being taken by the patient, atype of medication previously taken by the patient, and a patientmetabolic condition.
 31. The method of claim 30, wherein a firingcondition in the one or more firing conditions of a decision rule in theplurality of decision rules includes the patient metabolic condition andwherein the patient metabolic condition is classified on anon-dimensional scale.
 32. The method of claim 21, wherein the runningof all or a portion of the biochemical data set and the demographic dataset against the plurality of decision rules comprises determining alevel of β-cell stress in the patient.
 33. The method of claim 21,wherein the report further provides a first prioritization ofintervention classes in a first priority ordering of interventionclasses in a first priority ordering of a plurality of interventionclasses for a diabetic or pre-diabetic condition based on the identityof the patient pattern, wherein the plurality of intervention classescomprises one or more classes in the group consisting of a metforminclass, a sodium-glucose cotransporter-2 inhibitor class, a dipeptidylpeptidase-4 inhibitor class, an insulin class, a thiazolidinedioneclass, a glinides class, a sulfonylurea class, and a GLP-1 class. 34.The method of claim 33, wherein the first prioritization of interventionclass includes a prioritization of one or more drugs in a respectivedrug class.
 35. The method of claim 33, wherein the first prioritizationof intervention class includes a prioritization of one or more drugclasses, exercise, and diet.
 36. The method of claim 33, wherein thereport further provides a second prioritization of intervention class ina second priority ordering of the plurality of intervention classes forthe diabetic or pre-diabetic condition based on the identity of thepatient pattern.
 37. The method of claim 21, wherein the reportcomprises a plurality of sections, wherein each section in the pluralityof sections is classified as: a static section that includespredetermined information, a dynamic section that includes predeterminedinformation as determined by one or more decision rules in the pluralityof decision rules, or a reference section that includes informationprovided from one or more databases that is accessible to the computer.38. A computer system comprising: one or more processors; memory; andone or more programs, wherein the one or more programs are stored in thememory and configured to be executed by the one or more processors, theone or more programs including instructions for providing a healthcareprofessional with a metabolic status of a patient that is being testedor has been tested for a diabetic or pre-diabetic condition by a methodcomprising: obtaining a biochemical data set, in electronic form,wherein the biochemical data set comprises a plurality of test resultsfrom a single blood draw of the patient, wherein the plurality of testresults comprises at least three measurements from the group consistingof a high-sensitivity c-reactive protein test, an adiponectin leveltest, a proinsulin level test, an insulin level test, a C-peptide test,a HbA1c test, and an eGFR level test; obtaining a demographic data setfor the patient, in electronic form, wherein the demographic data setcomprises (i) an indication of a gender of the patient and (ii) anenumerated indication of a patient's stage of disease or a currenttherapy, wherein the enumerated indication of the patient's stage ofdisease or the current therapy is one of (a) diagnosed as pre-diabetes,(b) diagnosed with diabetes but not taking a drug (c) diagnosed withdiabetes and taking a first line diabetes drug (d) diagnosed withdiabetes and prescribed multiple diabetes drugs without insulin and (e)diagnosed with diabetes and prescribed multiple diabetes drugs withinsulin, or (f) a current therapy for the diabetic or pre-diabeticcondition; running all or a portion of the biochemical data set and thedemographic data set against a plurality of decision rules, wherein, inaccordance with a determination that one or more firing conditions ofeach respective decision rule in the plurality of decision rules isfired, a patient pattern is selected, from among a set of at least20,000 patient patterns, through the comparison of (i) the determinationthat one or more firing conditions of each respective decision rule inthe plurality of decision rules is fired to (ii) each patient pattern inthe set of at least 20,000 patient patterns, wherein the patient patterncomprises a pattern of insulin resistance, β-cell stress level, andcardiovascular inflammation; and preparing a report based on an identityof the patient pattern, in electronic form, wherein the report providesa metabolic status of the patient.
 39. A computer readable storagemedium storing one or more programs, the one or more programs comprisinginstructions, which when executed by an electronic device with one ormore processors and a memory cause the electronic device to perform amethod for providing a healthcare professional with a metabolic statusof a patient that is being tested or has been tested for a diabetic orpre-diabetic condition, comprising: obtaining a biochemical data set, inelectronic form, wherein the biochemical data set comprises a pluralityof test results from a single blood draw of the patient, wherein theplurality of test results comprises at least three measurements from thegroup consisting of a high-sensitivity c-reactive protein test, anadiponectin level test, a proinsulin level test, an insulin level test,a C-peptide test, a HbA1c test, and an eGFR level test; obtaining ademographic data set for the patient, in electronic form, wherein thedemographic data set comprises (i) an indication of a gender of thepatient and (ii) an enumerated indication of a patient's stage ofdisease or a current therapy, wherein the enumerated indication of thepatient's stage of disease or the current therapy is one of (a)diagnosed as pre-diabetes, (b) diagnosed with diabetes but not taking adrug (c) diagnosed with diabetes and taking a first line diabetes drug(d) diagnosed with diabetes and prescribed multiple diabetes drugswithout insulin and (e) diagnosed with diabetes and prescribed multiplediabetes drugs with insulin, or (f) a current therapy for the diabeticor pre-diabetic condition; running all or a portion of the biochemicaldata set and the demographic data set against a plurality of decisionrules, wherein, in accordance with a determination that one or morefiring conditions of each respective decision rule in the plurality ofdecision rules is fired, a patient pattern is selected, from among a setof at least 20,000 patient patterns, through the comparison of (i) thedetermination that one or more firing conditions of each respectivedecision rule in the plurality of decision rules is fired to (ii) eachpatient pattern in the set of at least 20,000 patient patterns, whereinthe patient pattern comprises a pattern of insulin resistance, β-cellstress level, and cardiovascular inflammation; and preparing a reportbased on an identity of the patient pattern, in electronic form, whereinthe report provides a metabolic status of the patient.